scholarly journals A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 528
Author(s):  
Yifan Xu ◽  
Qian Zhang ◽  
Jingjuan Zhang ◽  
Xueyun Wang ◽  
Zelong Yu
Keyword(s):  
Dynamic Model ◽  
Reconstruction Algorithm ◽  
The Body ◽  
Model Parameters ◽  
Reconstruction Method ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Vehicle Model ◽  
Body Velocity ◽  
Navigation Accuracy

The integrated navigation of inertial navigation systems (INS) and the Global Positioning System (GPS) is essential for small unmanned aerial vehicles (UAVs) such as multicopters, providing steady and accurate position, velocity, and attitude information. Nevertheless, decreasing navigation accuracy is a serious threat to flight safety due to the long-term drift error of INS in the absence of GPS measurements. To bridge the GPS outage for multicopters, this paper proposes a novel navigation reconstruction method for small multicopters, which combines the vehicle dynamic model and micro-electro-mechanical system (MEMS) sensors. Firstly, an induced drag model is introduced into the dynamic model of the vehicle, and an efficient online parameter identification method is designed to estimate the model parameters quickly. Secondly, the body velocity can be calculated from the vehicle model and accelerometer measurement. In addition, the nongravitational acceleration estimated from body velocity and radar height are utilized to yield a more accurate attitude estimate. Fusing the information of the attitude, body velocity, magnetic heading, and radar height, a navigation system based on an error-state Kalman filter is reconstructed. Then, an adaptive measurement covariance algorithm based on a fuzzy logic system is designed to reduce the weight due to the disturbed acceleration. Finally, the hardware-in-loop experiment is carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed navigation reconstruction algorithm aided by the vehicle model can significantly improve navigation accuracy during a GPS outage.

scholarly journals Tuning of the Wavelet Filters for the IMU Data Based on the PDC Method and the GPS Solution in a Bi-Dimensional Navigation Application

2019 ◽  
Vol 11 (3) ◽  
pp. 3-14
Author(s):  
Ioana Raluca ADOCHIEI ◽  
Teodor Lucian GRIGORIE ◽  
Felix Constantin ADOCHIEI
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Wavelet Filters ◽  
Time Frequency ◽  
Tuning Method ◽  
Coupling Dynamics ◽  
Navigation Accuracy ◽  
Sensor Errors

The degradation of navigation accuracy and integrity of GPS in the presence of radio frequency interference, hostile jamming and high dynamical situations, when the satellite signals may get lost due to signal blockage, led to the development of MEMS-INS/GPS integrated navigation systems for various applications of the positioning and navigation technologies. Unfortunately, the short-term advantages brought by the INS systems are overshadowed by their imprecise operation over the long term, mainly due to inertial sensor errors. A critical component of the inertial sensors errors is the noise. To improve the quality of the inertial sensors data, many denoising techniques have been used. Wavelet method has been proven as a useful tool for signal analysis, and it is widely used in signal processing and denoising applications. The here proposed technique is based on a time-frequency approach previously applied in bio-signals processing. In the proposed mechanism, the inertial sensors signals are processed analysed by using an extended version of the Wavelet transform. The optimal levels of decomposition are established for the wavelet filters, based on the evaluation of a parameter called coupling level (CL). It characterizes the coupling dynamics information between the reference signals, provided by a GPS, and the perturbed signal, which are the outputs of the inertial navigation system (INS). The proposed tuning method is experimentally tested in a bi-dimensional navigation application.

scholarly journals One approach to the integration of inertial and visual navigation systems

2005 ◽  
Vol 18 (3) ◽  
pp. 479-491
Author(s):  
Stevica Graovac
Keyword(s):  
Inertial Sensors ◽  
Angular Position ◽  
Visual Navigation ◽  
The Body ◽  
Moving Object ◽  
Coordinate Frame ◽  
Simultaneous Estimation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System

The algorithm of simultaneous estimation of motion parameters and scene structure using the integrated navigation system consisting from inertial sensors (three rate gyros and three accelerometers) and TV camera has been presented. All mentioned sensors are rigidly fixed to the body of a moving object. It is assumed that the inertial sensors are characterized by constant biases. The recognizable landmarks existing in the scene on known locations in the reference coordinate frame are assumed also. It is enabled by parallel processing of information in two independent navigation systems that they may correct each other, in order to estimate moving object?s linear and angular position relative to the landmark as well as it?s linear and angular velocities in an optimal fashion.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

scholarly journals A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2922
Author(s):  
Fan Zhang ◽  
Ye Wang ◽  
Yanbin Gao
Keyword(s):  
Fault Detection ◽  
Parametric Bootstrap ◽  
Integrated System ◽  
Range Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Tightly Coupled

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

Kinematic Analysis of Olympic Hurdle Performance: Women's 100 Meters

1985 ◽  
Vol 1 (2) ◽  
pp. 163-173 ◽  
Author(s):  
Ralph Mann ◽  
John Herman
Keyword(s):  
Elite Athlete ◽  
Olympic Games ◽  
Lower Leg ◽  
The Body ◽  
Performance Variables ◽  
Body Velocity ◽  
The Difference ◽  
Foot Position ◽  
Support Time ◽  
Leg Position

Selected kinematic variables in the performance of the Gold and Silver medalists and the eighth-place finisher in the women's 100-meter hurdles final at the 1984 Summer Olympic Games were investigated. Cinematographic records were obtained for all track hurdling events at the Games, with the 100-meter hurdle performers singled out for initial analysis. In this race, sagittal view filming records (100 fps) were collected at the 9th hurdle of the performance. Computer generated analysis variables included both direct performance variables (body velocity, support time, etc.) and body kinematics (upper leg position, lower leg velocity, etc.) that have previously been utilized in the analysis of elite athlete hurdlers. The difference in place finish was related to the performance variables body horizontal velocity (direct), vertical velocity (indirect), and support time (indirect). The critical body kinematics variables related to success included upper and lower leg velocity during support into and off the hurdle (direct), relative horizontal foot position (to the body) at touchdown into and off the hurdle (indirect), and relative horizontal foot velocity (to the body) at touchdown into the hurdle.

scholarly journals A faster escape does not enhance survival in zebrafish larvae

2017 ◽  
Vol 284 (1852) ◽  
pp. 20170359 ◽  
Author(s):  
Arjun Nair ◽  
Christy Nguyen ◽  
Matthew J. McHenry
Keyword(s):  
High Speed ◽  
Larval Fish ◽  
Body Position ◽  
Three Dimensional ◽  
Limited Range ◽  
The Body ◽  
Model Parameters ◽  
Fish Prey ◽  
Zebrafish Larvae ◽  
Fish Predators

An escape response is a rapid manoeuvre used by prey to evade predators. Performing this manoeuvre at greater speed, in a favourable direction, or from a longer distance have been hypothesized to enhance the survival of prey, but these ideas are difficult to test experimentally. We examined how prey survival depends on escape kinematics through a novel combination of experimentation and mathematical modelling. This approach focused on zebrafish ( Danio rerio ) larvae under predation by adults and juveniles of the same species. High-speed three-dimensional kinematics were used to track the body position of prey and predator and to determine the probability of behavioural actions by both fish. These measurements provided the basis for an agent-based probabilistic model that simulated the trajectories of the animals. Predictions of survivorship by this model were found by Monte Carlo simulations to agree with our observations and we examined how these predictions varied by changing individual model parameters. Contrary to expectation, we found that survival may not be improved by increasing the speed or altering the direction of the escape. Rather, zebrafish larvae operate with sufficiently high locomotor performance due to the relatively slow approach and limited range of suction feeding by fish predators. We did find that survival was enhanced when prey responded from a greater distance. This is an ability that depends on the capacity of the visual and lateral line systems to detect a looming threat. Therefore, performance in sensing, and not locomotion, is decisive for improving the survival of larval fish prey. These results offer a framework for understanding the evolution of predator–prey strategy that may inform prey survival in a broad diversity of animals.

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